
"""simple docstring"""






import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
					raise Exception("""requires fairseq >= 1.0.0a""")

logging.set_verbosity_info()
UpperCAmelCase        =				logging.get_logger(__name__)

UpperCAmelCase        =				"""Hello world! cécé herlolip"""




def __magic_name__	(				_lowerCamelCase:		str,					_lowerCamelCase:		str,					_lowerCamelCase:		bool			)					->						Any:

	'''simple docstring'''




	lowerCAmelCase												=  FairseqRobertaModel.from_pretrained(_lowerCamelCase			)
	roberta.eval()  # disable dropout
	lowerCAmelCase												=  roberta.model.encoder.sentence_encoder
	lowerCAmelCase												=  XLMRobertaConfig(
	    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,					hidden_size=roberta.cfg.model.encoder_embed_dim,					num_hidden_layers=roberta.cfg.model.encoder_layers,					num_attention_heads=roberta.cfg.model.encoder_attention_heads,					intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim,					max_position_embeddings=514,					type_vocab_size=1,					layer_norm_eps=1E-5,					)
	if classification_head:
		lowerCAmelCase												=  roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

	print('''Our RoBERTa config:''',					_lowerCamelCase			)

	lowerCAmelCase												=  XLMRobertaXLForSequenceClassification(_lowerCamelCase			) if classification_head else XLMRobertaXLForMaskedLM(_lowerCamelCase			)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	lowerCAmelCase												=  roberta_sent_encoder.embed_tokens.weight
	lowerCAmelCase												=  roberta_sent_encoder.embed_positions.weight
	lowerCAmelCase												=  torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight			)  # just zero them out b/c RoBERTa doesn't use them.

	lowerCAmelCase												=  roberta_sent_encoder.layer_norm.weight
	lowerCAmelCase												=  roberta_sent_encoder.layer_norm.bias

	for i in range(config.num_hidden_layers			):
		# Encoder: start of layer
		lowerCAmelCase												=  model.roberta.encoder.layer[i]
		lowerCAmelCase												=  roberta_sent_encoder.layers[i]

		lowerCAmelCase												=  layer.attention
		lowerCAmelCase												=  roberta_layer.self_attn_layer_norm.weight
		lowerCAmelCase												=  roberta_layer.self_attn_layer_norm.bias

		# self attention
		lowerCAmelCase												=  layer.attention.self
		assert (
		    roberta_layer.self_attn.k_proj.weight.data.shape
		    == roberta_layer.self_attn.q_proj.weight.data.shape
		    == roberta_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)			)
		)

		lowerCAmelCase												=  roberta_layer.self_attn.q_proj.weight
		lowerCAmelCase												=  roberta_layer.self_attn.q_proj.bias
		lowerCAmelCase												=  roberta_layer.self_attn.k_proj.weight
		lowerCAmelCase												=  roberta_layer.self_attn.k_proj.bias
		lowerCAmelCase												=  roberta_layer.self_attn.v_proj.weight
		lowerCAmelCase												=  roberta_layer.self_attn.v_proj.bias

		# self-attention output
		lowerCAmelCase												=  layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		lowerCAmelCase												=  roberta_layer.self_attn.out_proj.weight
		lowerCAmelCase												=  roberta_layer.self_attn.out_proj.bias

		# this one is final layer norm
		lowerCAmelCase												=  roberta_layer.final_layer_norm.weight
		lowerCAmelCase												=  roberta_layer.final_layer_norm.bias

		# intermediate
		lowerCAmelCase												=  layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		lowerCAmelCase												=  roberta_layer.fca.weight
		lowerCAmelCase												=  roberta_layer.fca.bias

		# output
		lowerCAmelCase												=  layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		lowerCAmelCase												=  roberta_layer.fca.weight
		lowerCAmelCase												=  roberta_layer.fca.bias
		# end of layer

	if classification_head:
		lowerCAmelCase												=  roberta.model.classification_heads['''mnli'''].dense.weight
		lowerCAmelCase												=  roberta.model.classification_heads['''mnli'''].dense.bias
		lowerCAmelCase												=  roberta.model.classification_heads['''mnli'''].out_proj.weight
		lowerCAmelCase												=  roberta.model.classification_heads['''mnli'''].out_proj.bias
	else:
		# LM Head
		lowerCAmelCase												=  roberta.model.encoder.lm_head.dense.weight
		lowerCAmelCase												=  roberta.model.encoder.lm_head.dense.bias
		lowerCAmelCase												=  roberta.model.encoder.lm_head.layer_norm.weight
		lowerCAmelCase												=  roberta.model.encoder.lm_head.layer_norm.bias
		lowerCAmelCase												=  roberta.model.encoder.lm_head.weight
		lowerCAmelCase												=  roberta.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	lowerCAmelCase												=  roberta.encode(_lowerCamelCase			).unsqueeze(0			)  # batch of size 1

	lowerCAmelCase												=  model(_lowerCamelCase			)[0]
	if classification_head:
		lowerCAmelCase												=  roberta.model.classification_heads['''mnli'''](roberta.extract_features(_lowerCamelCase			)			)
	else:
		lowerCAmelCase												=  roberta.model(_lowerCamelCase			)[0]
	print(our_output.shape,					their_output.shape			)
	lowerCAmelCase												=  torch.max(torch.abs(our_output - their_output			)			).item()
	print(F"""max_absolute_diff = {max_absolute_diff}"""			)  # ~ 1e-7
	lowerCAmelCase												=  torch.allclose(_lowerCamelCase,					_lowerCamelCase,					atol=1E-3			)
	print('''Do both models output the same tensors?''',					'''🔥''' if success else '''💩'''			)
	if not success:
		raise Exception('''Something went wRoNg'''			)

	pathlib.Path(_lowerCamelCase			).mkdir(parents=_lowerCamelCase,					exist_ok=_lowerCamelCase			)
	print(F"""Saving model to {pytorch_dump_folder_path}"""			)
	model.save_pretrained(_lowerCamelCase			)


if __name__ == "__main__":
					UpperCAmelCase        =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
					)
					UpperCAmelCase        =				parser.parse_args()
					convert_xlm_roberta_xl_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)




"""simple docstring"""






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
					import torch



@require_torch
@require_optimum
@slow
class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  tokenizer('''This is me'''      ,return_tensors='''pt'''			)

				lowerCAmelCase												=  model.to_bettertransformer()

				self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				lowerCAmelCase												=  model.generate(**SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.reverse_bettertransformer()

				self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

					self.assertFalse(
					    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

					lowerCAmelCase												=  model_reloaded.generate(**SCREAMING_SNAKE_CASE_			)
					self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)






			def 					UpperCAmelCase							(self  :	str			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
					with self.assertRaises(SCREAMING_SNAKE_CASE_			):
						model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  model.reverse_bettertransformer()
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)


